In comparing the number of rules in cross-cleaning with that in the cutoff by frequency, the former is three times higher than the latter.

ambiguities, which occur during parsing or mapping, are resolved by selecting the rules that minimize the semantic distance between the input words and source examples (real examples in the training corpus) of the transfer rules (Furuse and Iida, 1994).


This table shows that the test corpus BLEU score and the subjective Focusing on the subjective quality of the proposed methods, some MT results were degraded from the baseline due to the removal of rules.

we divided it into sub-corpora for training, evaluation, and test as shown in Table 1.


Moreover, from the "docsrc" field (which records the document id that each training or test example originates) of the official SENSEVAL-2 training and test examples, we realized that there are many cases when some of the examples from a document are used as training examples, while the rest of the examples from the same document are used as test examples.

eliminating the advantage that manually sense-tagged data have in their sense coverage would reduce the performance gap between the two approaches from 0.140 to 0.065.


The supervised classifiers were built using TiMBL version 4.2 (Daelemans et al., 2002), a memory-based classification system based on the knearest neighbour algorithm.

the second class is uncountable: COMLEX's NCOLLECtIVE or :PLURAL *NONE* and ALt-J/E's strongly and weakly countable and uncountable.


An Eojeol is a surface level form consisting of more than one combined morpheme.

in order to reflect word spacing information in Equation 2, which is very useful in Korean POS tagging, Equation 2 is changed to Equation 3 which can consider the word spacing information by calculating the transition probabilities like the equation used in Kim et al.


In this case, we estimate the POS category by using transformation rules.

too much information often leads to overtraining the model.


Predicting prosody is known to be a hard problem that is thought to require information on syntactic boundaries, syntactic and semantic relations between constituents, discourse-level knowledge, and phonological well-formedness constraints (Hirschberg, 1993).

from the results displayed in Table 2, the following can be concluded.


the wider context is required to compensate for the free order of Korean, but it hurts the performance (Cherkassky and Mulier, 1998).


If we use all data for both the rules and memory-based learning, we have to weight the methods to combine them.

unless we concentrate on the postpositions, we must enlarge the neighboring window to get a good hypothesis.


We use the left-to-right SNoW model in these experiments.

intuitively, it is the local normalization that makes distribution mass of the transitions leaving a given state incomparable with all other transitions.


The occurrence of join has influence on the lexical selection of as.

the model was tested on WSJ section 20 of PtB, and trained on section 0 through 24 except section 20.


As far as supertagging is concerned, word context forms a very large space.

one method is to skip the local normalization step, and the other is to combine the results of left-to-right scan and right-to-left scan.


It is noted in (Muñoz et al., 1999) that SNoW's output provides, in addition to the prediction, a robust confidence level in the prediction, which enables its use in an inference algorithm that combines predictors to produce a coherent inference.

this makes probabilities comparable only within the transitions of the same history h .


Both of these models can be called grammar-based models, in that they capture the syntactic structure of a sentence, and the model parameters are estimated from syntactically annotated corpora such as the Penn Treebank.

in order to deal with the data sparseness problem, we used a backoff scheme (Katz, 1987) for parameter estimation.


The optimal value of N is 3 in our experiments.

next, we describe the elements of the DLM that assign probability to each possible W and its most probable D, P(W, D).


The syntactic structure of the TIGERsentences can be accessed in a separate viewer.

it is not always the case that frame structure parallels syntactic structure.


The current on-line version of the frame database (Johnson et al., 2002) consists of almost 400 frames, and covers about 6,900 lexical entries.

most recent work in corpus-based semantics has taken an unsupervised approach, relying on statistical methods to extract semantic regularities from raw corpora, often using information from ontologies like WordNet (Miller et al., 1990).


The Cartesian product over the features in S i and S i−1 is an attempt to capture inter-sentential dependencies.

the metric ranges from −1 (inverse ranks) to 1 (identical ranks).


We conclude with a discussion in Section 5.

they are significantly better than all other features (α = 0.05).


Then, it looks at the first nonverbal behavior occurring during the pause after the UU.

in summary, it is already known that eye gaze can signal a turn-taking request [16], but turntaking cannot account for all our results.


(1) MACK can continue giving the directions as normal, by sending on the next segment in the Agenda to the GM.

in previous models, information is grounded only when a listener returns verbal feedback, and acknowledgement marks the smallest scope of grounding.


L n their respective lengths, 4 and L the length of the text, the adapted metric is expressed as follows, combining frequency (f req(t i )) of a term t i and the compactness of its underlying chain: In the second part of the algorithm, we combine information from all term repetitions to compute a lexical cohesion score at each sentence break (or, in the case of spoken conversations, speaker turn break).

we ignore all potential boundaries with a probability lower than p limit .


If a given input does not satisfy these hard constraints, the unification fails.

the temporal relation between referring expressions (i.e., γ mn ) is Next, we discuss how these definitions and the process of graph matching address optimization, in particular, with respect to key principles of Optimality theory for natural language interpretation.


input and conversation context, it can be easily extended to incorporate other modalities such as gaze inputs.

currently we have not yet addressed the strict dominance aspect of Optimality Theory.


There is a considerable chance that the predicate ball is chosen and hence ball(?obj) is abductively inferred as the meaning of "ball" resulting in a rule like [4].

these new constructions should be such that there is a high chance that the hearer may be able to guess their meaning.


A survey of work which follows a similar methodology is found in (CangelosiParisi, 2003).

we implement artificial agents with components modeling certain cognitive operations (such as introducing a new syntactic category, computing an analogy between two events, etc.


Further distinction is drawn into inner participants, such as Actor, Patient, Addressee, and free modifications, such as Location, Means, Direction.

in this section, we first briefly describe the corpus collection experiment and then present the common language phenomena found in the corpus.


TAGs provide a systematic way of formalizing the channel model, and their polynomialtime dynamic programming parsing algorithms can be used to search for likely repairs, at least when used with simple language models like a bigram language model.

when a repair has begun, the channel model incrementally processes the succeeding words from the start of the repair.


Interestingly, if we trace the temporal word string through this dependency structure, aligning words next to the words they are dependent on, we obtain a "helical" type of structure familiar from genome models, and in fact TAGs are being used to model genomes for very similar reasons.

the probabilistic model of repairs explored here seems to be most successful in identifying long repairs in which the reparandum and repair are similar enough to be unlikely to have been generated independently.


In the next section of the paper we describe how global linear models can be applied to speech recognition.

the features in the model are updated, and the algorithm moves to the next utterance.


Simulations have helped diagnose initial problems overlooked in the rule-based mechanisms for context tracking; this has served to ensure correct inheritance of attributes given the many permutations of sequences of input sentences that are possible within a single conversation.

following this, Genesis provides a summary of the characteristics of the data set, utilizing context information provided by the dialog manager and the frequency statistics.


(2) Sentfeatures: These features are derived from the DSyntSs, and describe the deep-syntactic structure of the utterance, including the chosen lexemes.

the rules are applied locally, across adjacent sequences of utterances (Brennan et al., 1987).


all interviewees agreed on one major theme, namely that the system should be as friendly and reassuring as possible towards novices.


In general we would expect that estimating the joint probability would be harder than estimating the conditional probability, because the joint probability contains more information than the conditional probability.

designing a history-based model of parsing involves two steps, first choosing a mapping from the set of phrase structure trees to the set of parses, and then choosing a probability model in which the probability of each parser decision is conditioned on the history of previous decisions in the parse.


Because these words have not yet been reached by the parse, we cannot assign them any structure, and thus the estimation process has no way of knowing what words in this string will end up being relevant to the next decision it needs to make.

the unbounded nature of the parsing problem means that the individual parameters of the discriminative model are much harder to estimate than those of the generative model.


If the performance is improved by using a larger n, this means that significant features do exist.

this indicates that our method is very robust as regards substructure size; It therefore becomes unnecessary for us to decide sub-structure size carefully.


If measured by Success, the improvement is more apparent by 4.7% (MUC-6) and 1.8% (MUC-7).

we found that for both MUC-6 and MUC-7 data set, the algorithm terminated in the second round.


Apart from basic features in Table 1, composite features can be generated by taking conjunction of basic features.

morton (2000) also uses a maximum entropy mention-pair model, and a special "dummy" mention is used to model the event of starting a new entity.


The principle of software modularity calls for local optimization.

since our goal is to evaluate the effect of anaphoricity information on coreference resolution, we make no attempt to modify our system to adhere to the rules specifically designed for ACE.


For instance, it is possible to represent anaphoricity as a real-valued feature indicating the probability of an NP being anaphoric rather than as a binary-valued feature.

for training globally-optimized anaphoricity models, RIPPER is always used in conjunction with Method 1 and Max-Ent with Method 2, as described in Section 2.2.


In order words, on average, for each English unit, we have 1.53 = 5,640/3,683 Chinese correspondences.

n-gram TM is a successful realization of DOM paradigm.


This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM).

we estimate a joint probability model that can be easily marginalized in order to yield conditional probability models for both transliteration and back-transliteration.


The segmentation step correctly segmented the romanji to "matsu-da".

in this section, we describe the gold standards and evaluation measures for evaluating the effectiveness of the above method for backtransliterating Japanese names.


a closer investigation of this area shows that the best settings are D=0.575 and S=0.15.


To better reflect overall performance in all template slots, we introduced a threshold of similarity of 65% for comparison between a golden standard slot entry and the one provided by the application.

careful analysis of this extensive corpus presented some interesting facts about what we have termed "Explicit Metalinguistic Operations" (or EMOs) in specialized discourse: A) EMOs usually do not follow the genusdifferentia scheme of aristotelian definitions, nor conform to the rigid and artificial structure of dictionary entries.


Non-statistical optimizations can be seen as a first step toward a highly efficient parser, while statistical optimization can be applied as a second step.

the large and complex structure of tFS descriptions (Carpenter, 1992) leads to slow unification times, affecting the parsing times.


An intuitive example (real-life examples cannot be reproduced here -a category in a typical TFSG can have hundreds of nodes) is presented in Figure 4.  can promote only to t 7 , thus x 2 and y 2 will always be compatible).

future work will focus on using static analysis to determine smaller representations (by cutting nodes in Static Cuts) of the chart edges themselves.


When the training of the head-driven parsing model was extended to the BLLIP 1987 corpus (20M words), the combination of models (α ¡ 1¦ 16 β ¡ 1) achieved additional improvement in WER over the lattice trigram alone.

the conditioning context of the parsing model parameters includes POS tagging.


In practice, these heuristics have a negative effect on parse accuracy, but the amount of pruning can be tuned to balance relative time and space savings against precision and recall degradation (Collins, 1999).

sparse data problems arising from the limited availability of annotated training data become a problem.


As is normally the case in CLP, deductive search is used alongside constraint resolution.

this paper identifies what it is precisely about this extra functionality that we miss (modularity, Section 2), determines what it would take at a minimum computationally to get it back (delaying, Section 3), and attempts to measure exactly how much that minimal computational overhead would cost (about 4 µs per delay, Section 4).


Every word has some affinity to the context, and the context can be represented by a vector indicating the affinity of each word to it.

this data set contains web pages gathered from university computer science departments.


Among centroid-contexts, some contexts could not have good features of a category even though they include the keywords of the category.

since text categorization is a task based on pre-defined categories, we know the categories for classifying documents.


The final section describes conclusions and future works.

we report performances from two kinds of Naive Bayes classifiers which are learned from 500 training documents and the whole training documents respectively.


Bayes' Theorem provides a convenient framework for predicting a binary response s ∈ {−1, 1} from a feature vector x: For a labeled sample of data (x i , s i ), i = 1, ..., n, a class's marginal probability π k can be estimated trivially as the proportion of training samples belonging to the class.

the corresponding drawback to such an approach is that its predictions are not validated on actual documents.


One task in opinion extraction is to label a review document d according to its prevailing sentiment s ∈ {−1, 1} (unfavorable or favorable).

the estimated logit is This is a positive multiple of Turney's classifier (Equation 3), so they clearly match in sign.


As with document-level polarity classification, we could perform subjectivity detection on individual sentences by applying a standard classification algorithm on each sentence in isolation.

this can prevent the polarity classifier from considering irrelevant or even potentially misleading text: for example, although the sentence "the protagonist tries to protect her good name" contains the word "good", it tells us nothing about the author's opinion and in fact could well be embedded in a negative movie review.


Each word in WordNet can belong to multiple synsets in the hierarchy, which are its possible senses.

the other significant difference between the models is in the constraints on the parameters and the effect that they have on sense disambiguation.


Purity and normalized mutual information were used to evaluate the clustering performance on 12 Chinese verbs.

we use two external quality measures, purity and normalized mutual information (NMI) (Strehl.


A single cluster with all instances for a target verb has a zero MI.

simple lexical or POS tag features sometimes fail to capture such information, which can be seen clearly in (2), 0 iff the target verb has no sentential complement 1 iff the target verb has a nonfinite sentential complement 2 iff the target verb has a finite sentential complement 去年 出 (2) a.


The ultimate goal of ¢ ¡ ¤ £ £ ¦ £ is to alleviate the data labelling bottleneck by means of a trade-off between quality and quantity of the training data.

the automatic sense tagging for Mt and SV2LS tR training data is based on using SALAAM with machine translated parallel corpora.


The supervised WSD system chosen here is the University of Maryland System for SENSEVAL 2 Tagging ( ¢ ¡ ¤ £ ¥ £ § ¦ ) (Cabezas et al.

in the study, Mihalcea compares results yielded by the supervised learning system trained on the automatically generated data, GenCor, against the same system trained on manually annotated data.


For a phrase structure parser, we first convert the produced phrase structures into dependency graphs in a straightforward way, and then apply a sequence of graph transformations: changing dependency labels, adding new nodes, and adding new dependencies.

the constituent's label and headword should be correct, but not necessarily the span.


leftmost NP to right of V head of VP type rule is likely to be an object etc.


2 NEGRA's original annotation is as dependency trees with phrasal nodes, crossing branches, and no empty elements.

9,10 Note that this evaluation metric does not require correct attachment of the empty category into 8 A complete description of feature templates can be found at http://nlp.stanford.edu/˜rog/acl2004/templates/index.html 9 For purposes of comparability with Johnson (2002) we used Charniak's 2000 parser as P .


Kernel (PAK) Given the semantic objects defined in the previous section, we design a convolution kernel in a way similar to the parse-tree kernel proposed in (Collins and Duffy, 2002).

in this paper, we select portions of syntactic trees, which include predicate/argument salient sub-structures, to define convolution kernels for the task of predicate argument classification.


The main drawback is that structures may not be properly represented by flat features.

the results have shown that: First, SVMs using the above kernels are appealing for semantically parsing both corpora.


This suggests that PAF generates the same information as the standard features in a linear space.

the classical solution adopted for such tasks is to convert syntax structures into flat feature representations which are suitable for a given learning model.


Note however that according to the χ 2 test there is no significant difference between the oracle system and TiMBL with optimized parameters.

we thank Nuance Communications Inc. for the use of their speech recognition and synthesis software and Alexander Koller and Dan Shapiro for reading draft versions of this paper.


On the one hand, accepting misrecognized hypotheses leads to misunderstandings and unintended system behaviors which are usually difficult to recover from.

we also annotate all misrecognized hypotheses of in-grammar utterances with their respective wER scores.


3 We use a threshold (50%) on a hypothesis' WER as an indicator for whether hypotheses should be clarified or rejected.

one would ideally set a threshold directly on CA scores for this labeling, but these are currently not available for our data.


Moreover, it is no longer the case that every feature  Table 9: %Corr., NPN Consensus, MAJ=48.35% set performs as well as or better than their baselines 6 ; within the "-id" sets, NnN "sp" and EnE "lex" perform significantly worse than their baselines.

across schemes, our feature sets outperform a majority baseline, and lexical features outperform acoustic-prosodic features.


Fifth, our method associates conditional probabilities with frames.

one of these, (Schulte im Walde, 2002), induces a computational subcategorisation lexicon for over 14,000 German verbs.


We extract non-empty semantic forms 2 for 3586 verb lemmas and 10969 unique verbal semantic form types (lemma followed by non-empty argument list).

the system of (Briscoe and Carroll, 1997) distinguishes 163 verbal subcategorisation classes by means of a statistical shallow parser, a classifier of subcategorisation classes, and a priori estimates of the probability that any verb will be a member of those classes.


This in turn indicates that simply avoiding nocb transitions is more relevant to text structuring than the combinations of the other Centering notions that the more complicated metrics make use of.

(2) <unit finite='finite-yes' id='u210'> <ne id="ne410" gf="subj">144</ne> is <ne id="ne411" gf="predicate"> a torc</ne> </unit>.


this problem is strictly easier than TSP or discourse ordering.

since it is known that there can be no polynomial algorithms that compute -approximations for general ATsP, for any (Cormen et al., 1990), this means that dPDOP cannot be approximated either (unless P=NP): Any polynomial algorithm for dPDOP will compute arbitrarily bad solutions on certain inputs.


With this assumption, GATSP can be phrased as an ILP as follows (this formulation is similar to the one proposed by Laporte et al.

a Majority Ordering algorithm is not guaranteed to compute good solutions to the discourse ordering problem, as Lapata (2003) assumes.


The algorithm also takes as input a *preferred-attributes* list that contains, in order of preference, the attributes that human writers use to reference objects.

iF a nom is similar to the head noun of the object of any relation of e i THEN (a) SQ = SQ + 4 (b) flatten that relation for e i , i.e., add the attributes of the object of the relation to the attribute list for e i in step 2, we compare a nominal attribute a nom of e o to the head noun of the object of a relation of e i .


Now, if SQ is non-zero, the nominal attribute a nom has bad discriminating power and we set DQ = −SQ.

the first mention NP for that reference.


One (inelegant) solution would be to introduce a new predicate allegedMurderer1(x).

this only works where the gradation is along one axis; in particular, this approach will not work for colours in general, and cannot be used to deduce the relative similarity between yellow and orange as compared to, say, yellow and blue.


*,", "and" and "or" for parallel expressions, and the pattern ") --" (used in datelines at the beginning of articles) as peculiar to The New York Times.

the types of both arguments of a relation are the same in the COM-COM domain.


If φ m (t i ) = φ m (t j ), then we declare the two nodes completely dissimilar.

in traditional machine learning, we are provided a set of training instances S = {x 1 .


We assume here that there is a single relation for each sentence between the entities 5 .

dynamic model (D3) Figure 1: Models for role and relation extraction.


In today's parsers rule bodies do not exist a priori but are generated on the fly, usually with methods based on n-grams, which are one particular way of inducing probabilistic regular languages.

we discuss related work in Section 6 and conclude in Section 7.


It is also related to loglinear models for machine translation (Och, 2003).

it is often implicitly defined in a given amount of application data (called adaptation data) from which the specific standard can be partially learned.


An obvious issue is that one does not expect the phrase structure representation of English (as produced by state-of-the-art treebank parsers) to carry over to less configurational languages.

(Koehn et al., 2003) show that exploiting all contiguous word blocks in phrase-based alignment is better than focusing on syntactic constituents only.


For test sets, we carry over those from the perplexity experiments (see Footnote 6, Section 4), which are derived from CPC, EJP, and PAT.

so regression may backfire when trained on wrong data.


We take it here that the business of MEMT is about choosing among translation outputs from multiple MT systems, whether black box or not, for each input text.

recall that our goal here is to find how the V-by-M affects performance of MEMT on tCPC, tEJP, and tPAT.


Let T 1 : Σ * ×Ω * → K and T 2 : Ω * ×Γ * → K be two transducers defined over the same semiring K. Their composition T 1 • T 2 realizes the function T : Σ * ×Γ * → K and the theory has been described in detail in (Pereira and Riley, 1996).

in Section 4.1 we will compare the efficiency of different toolkits.


In comparison to parallel corpora, ie corpora which are mutual translations, comparable corpora have not received much attention from the research community, and very few methods have been proposed to extract bilingual lexicons from such corpora.

translate the context vectors with a general bilingual dictionary D, accumulating the contributions from words that yield identical translations.


Compilation of translation lexicons is a crucial process for machine translation (MT) (Brown et al., 1990) and cross-language information retrieval (CLIR) systems (Nie et al., 1999).

in Figure 1 (a) it contains the translations "喬治布希" (George Bush) and "布 希" (Bush) obtained from the pages in Taiwan.


The definition of p A in the proof of Theorem 5 relies on the strings output by A.

by Lemma 4, we can construct a new PCFG (G , p G ) that is proper and consistent, and such that p G (d) = p G (d), for each complete derivation d of G .


Consider a PCFG with rule/probability pairs: There are two key transitions in the associated LR automaton, which represent shift actions over c and d (we denote LR states by their sets of kernel items and encode these states into stack symbols): Assume a proper assignment of probabilities to the transitions of the LR automaton, i.e., the sum of transition probabilities for a given LR state is 1.

a probabilistic parsing strategy is defined to be a function S that maps a reduced, proper and consistent PCFG (G, p G ) to a triple S(G, p G ) = (a, p a , f ), where (a, p a ) is a reduced, proper and consistent PPDT, with the same properties as a (non-probabilistic) parsing strategy, and in addition: • For each complete derivation d and each complete computation c such that a complete computation has the same probability as the complete derivation that it is mapped to by function f .


Furthermore, commonly used training algorithms for PCFGS/PPDAs always produce proper probability assignments, and many desired mathematical properties of these methods are based on such an assumption (Chi and Geman, 1998;Sánchez and Benedí, 1997).

p A (τ ) > 0 for each transition τ , and p A (c) > 0, where c is the above-mentioned dead computation.


We will take a somewhat less strict view and rule out the adjunction of the publish tree simply on the grounds that it would involve attaching a structure rooted in (or possibly CP) to a DP node.

a complete theory of syntax incorporating them would be (not necessarily not) compatible with implementation within existing TAG-based systems.


systems are especially useful to obtain a specific piece of information without the need of manually going through all the available documentation related to the topic.

we have designed a layer oriented to process the different types of temporal questions.


(and/or "What award did <candidate> win in 1970?")

our approach seems most beneficial to middle-performance levels, which, by inspection of TREC results for the last 5 years, is where the leading systems currently lie.


1): it separates conjunctions and verbs relating to causality from others.

events can be classified according to their inherent temporal characteristics, such as the degree of telicity and/or atomicity (Li and Wong, 2002).


BatchSet with the maximal size K. INTERSet with the maximal size M Steps :  • Strategy 2: (Figure 4) We combine the informativeness and representativeness criteria using the functio , in which the Info and Density value of NE i are normalized first.

another interesting work is to study when to stop active learning.


In the machine learning approaches of natural la nguage processing (NLP), models are generally trained on large annotated corpus.

the named entities in the same cluster may be considered similar to each other, so we will select the named entities from different clusters at one time.


2003) and named entities are less and distributed much sparser in the newswire texts than in the biomedical texts.

(Brinker 2003) first incorporate diversity in active learning for text classification.


Corpus I contains mention pairs of the same names; these are the most frequently mentioned names in the document pool.

in our research, the algorithm directly models the pairwise context similarity.


5): 2 Based on our data analysis, there is no observable difference in linguistic expressions involving frequently mentioned vs. occasionally occurring person names.

in our experiments, we used ~350,000 AP and WSJ news articles (a total of ~170 million words) from the TiPSTER collection.


Longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring insequence n-grams automatically.

we can find the LCS of two sequences of length m and n using standard dynamic programming technique in O(mn) time.


ROUGE-L, ROUGE-W, ROUGE-S*, ROUGE-S4, and ROUGE-S9 were equal performers to BLEU in measuring fluency.

although BLEU has been shown to correlate well with human assessments, it has a few things that can be improved.


Notably, Recall of weighted N-grams is found to be a good estimation of human judgements about translation Adequacy.

table 1 present the baseline results for nonweighted Precision, Recall and F-score.


BLEU scores go in line with Candide's Fluency scores, but do not account for its Adequacy scores.

the baseline BLEU approach fails to consistently predict scores for Adequacy.


Let V denote a M × M second-order cooccurrence symmetric matrix.

these lexical resources often miss domain specific word senses, even many new words are not included inside.


As shown in Table 6, the highest-achieving SVM model is also able to obtain higher accuracies than the naïve Bayes and maximum entropy models.

the training and testing examples can be represented as a set of binary vectors: each row shows the correct class c for an observed vector x of five dimensions.


The PCFG parser is then trained, using the enhanced strings as input, without inserting any additional empty nodes.

in the sentence below, who is coindexed with the empty category *T* in the embedded S; the function tag SBJ indicates that this empty category is the subject of that S: Empty categories, with coindexation and function tags, allow a transparent reconstruction of predicate-argument structure not available from a simple bracketed string.


3 We are not aware of any multitreebanks at this time.

note that there is one inference per internal node of the multitree.


The generalization is achieved by allowing context-free productions to rewrite tuples of strings, rather than single strings.

independent rewriting is also useful for modeling paraphrasing.


While Sun was concerned with predicting accented syllables, some of the same variables apply to word level targets as well.

labelers were instructed to use duration, amplitude, pausing information, and changes in f0 to identify events.


and, but, whereas after, as soon as, before, ever since, now, now that, once, until, when, whenever although, because, even though, for, given that, if, if ever, in case, on condition that, on the assumption that, on the grounds that, provided that, providing that, so, so that, supposing that, though, unless Table 3: Discourse markers used in the type experiment The data for the experiments comes from a database of sentences collected automatically from the British National Corpus and the world wide web (Hutchinson, 2004).

the first, 1 Nearest Neighbour (1NN), is an instance based classifier which assigns each marker to the same class as that of the marker nearest to it.


For DMs + rb, DMs + vb and DMs + rb + vb we also tried weighting the cooccurrences so that the sums of the co-occurrences with each of verbs, adverbs and discourse markers were equal.

secondly, we would like to see how far our results can be extended to include adverbial discourse markers, such as instead or for example, by using just features of the clauses they occur in.


We present two general strategies for generating useful auxiliary problems: one in a completely unsupervised fashion, and the other in a partiallysupervised fashion.

the training and test data sets consist of the Wall Street Journal corpus (WSJ) sections 15-18 (212K words) and section 20, respectively.


until converge the predictor space (corrupted with estimation error, or noise), then SVD-ASO can be interpreted as finding the "principle components" (or commonality) of these predictors (i.e., "what good predictors are like").

(2004) used word-cluster memberships induced from an unannotated corpus as features for named entity chunking.


A related idea is to use Expectation Maximization (EM) to impute labels.

the additional information discovered by SVD-ASO appears crucial to achieve appreciable improvements.


In general, this work has demonstrated the susceptibility of CRFs to overfit the training data during parameter estimation.

these are based on those found in (Curran and Clark, 2003).


We call this model a logarithmic opinion pool (LOP) of CRFs (LOP-CRFs), and provide a procedure for learning the weight of each expert in the product.

of this, the normalisation constant for the LOP-CRF can be calculated efficiently via the usual forward-backward algorithm for CRFs.


For example, abstraction subsumes the lex-files attribute, quantity, relation, communication and time.

if the following phrase is a PP then an indirect object (iobj) relation is created.


The limited coverage of lexical-semantic resources is a significant problem for NLP systems which can be alleviated by automatically classifying the unknown words.

such a filter turned out to make little difference.


Accuracy orig 0.564 orig*fset 0.587 orig+pb 0.597 (orig+pb)*fset 0.628 Table 5: Accuracy of system on WordNet sensetagging of 20 Senseval-2 verbs with more than one frameset, with and without gold-standard frameset tag.

3 Furthermore, only one of the matched objects needs to be specified, as in Example 3 where the second matched object (presumably the company's prices) is unstated.


Some symptoms you have seen conflict with that theory.

as regards evaluation, NLG systems have been evaluated e.g.


The work we present in this paper addresses three issues: evaluation of Natural Language Generation (NLG) systems, the place of aggregation in NLG, and NL interfaces for Intelligent Tutoring Systems.

what follows should be taken as observations rather than as rigorous findings -useful observations they clearly are, since sible.


Real-world text-to-text generation tasks, such as headline generation and machine translation, are likely to be handled graciously in this framework, as the complexity of IDL-expressions for these tasks tends to be lower than the complexity of the IDL-expressions we worked with in our experiments.

in what follows, we denote a state of  .


Future work includes the investigation of such features, as well as the abstraction of lexical dependencies like semantic classes.

the reference distribution method achieved higher accuracy and lower cost.


Hence, we map the tuple Ñ Ð Ö , which corresponds to Ñ Ð Ö , into a conjunctive node.

despite research on HPSG processing efficiency (Oepen et al., 2002a), the application of HPSG parsing is still limited to specific domains and short sentences (Oepen et al., 2002b;.


The model based on MIRA also performs well on Czech, again slightly outperforming averaged perceptron.

for POS tags we used the automatically generated tags in the data set.


Their conjecture is that the majority of the features of the more specific word are included in the features of the more general one.

we thank Idan Szpektor for providing the infrastructure system for web-based data extraction.


by Hypothesis II if all the features of w are included by v then we expect that w entails v. We observed that Hypothesis I is better attested by our data than the second hypothesis.


To estimate the empirical validity of the defined hypotheses we developed an automatic inclusion testing algorithm (ITA).

our manual analysis shows, that over 90% of the first case pairs were due to a different sense of one of the entailing word, e.g.


Figure 2 summarizes our average 10-fold crossvalidation accuracy results.

(Koppel and Schler (2005) in independent work also discuss various types of neutrality.)


5 If we choose R from a family of sufficiently "gradual" functions, then similar items necessarily receive similar labels.

(Abusing notation, since we're already playing fast and loose: [3c]: baseline 52.4%, reg 61.4%, reg In future work, it would be interesting to determine author-independent characteristics that can be used on (or suitably adapted to) data for specific authors.


Given the enriched ontologies produced by our method, we believe that ontologizing lexicalsemantic resources will be feasible.

when the recursion meets a non-leaf node, like chairwoman in Figure 2, the algorithm first recursively applies itself to each of the node's children.


For example, consider the example verb relation "to surpass isstronger-than to hit" from above.

in this section, we provide a quantitative and qualitative evaluation of our framework.


If we had a large collection of sensetagged text, then we could extract disambiguated feature vectors by collecting co-occurrence features for each word sense.

by design of the propagation algorithm, each concept node feature is shared by at least two of its children.


Using the algorithm presented in Section 4, we induced ontological feature vectors for the noun nodes in WordNet using the lexical co-occurrence features from the TREC-2002 corpus.

these representations do not distinguish between the different senses of words.


Our model uses only the tendency of having the same orientation.


Consisting of 173,252 Wall Street Journal articles from the year 1988 to 1992, the data set contained about 76 million words.

in this section, we introduce in detail how to compute the semantic compatibility, using the predicateargument statistics obtained from the corpus or the web.


For each candidate, its semantic compatibility with the anaphor could be represented simply in terms of frequency where count(candi, ana) is the count of the tuple formed by candi and ana, or alternatively, in terms of conditional probability (P (candi, ana|candi)), where the count of the tuple is divided by the count of the single candidate in the corpus.

previous approaches to anaphora resolution adopt the singlecandidate model, in which the resolution is done on an anaphor and one candidate at a time (Soon et al., 2001;Ng and Cardie, 2002).


Corpus-based knowledge usually suffers from data sparseness problem.

so far the web has been utilized in nominal anaphora resolution (Modjeska et al., 2003;Poesio et al., 2004) to determine the semantic relation between an anaphor and candidate pair.


We see that used in isolation, the semantic compatibility feature is able to achieve a success up to 61% around, just 4% lower than the best indicative feature FirstNP.

as the purpose of the predicate-argument statistics is to evaluate the preference of the candidates in semantics, it is possible that the statistics-based semantic feature could be more effectively applied in the twincandidate (Yang et al., 2003) that focusses on the preference relationships among candidates.


The Collins parser (Collins, 1997) does use dynamic programming in its search.

word (49,097) The instances of this schema are lexical items together with the categories of of their immediate ancestor nodes, where is a schema parameter ( = 2 or = 3 here).


1 and 2 are parameters of this schema; here 1 = 1 or 1 = 2 and 2 = 1.

the 50 best (i.e., the 50 highest probability) parses of a sentence often contain considerably better parses (in terms of f -score); this paper describes a 50-best parsing al-gorithm with an oracle f -score of 96.8 on the same data.


The efficiency of dynamic programming means that the entire space of parses can be considered, not just a candidate list.

we would expect further improvement if we used different models to derive the kernel and to gener-ate the candidates, thereby exploiting the advantages of combining multiple models, as do the better performing methods using kernels.


The critical difference is the definition of margin or the way they regularize the vector w. ( Rätsch, 2001) shows that the iterative feature selection performed in boosting asymptotically realizes an l 1 -norm ||w|| 1 regularization.

tree kernel uses the all-subtrees representation not explicitly but implicitly by reducing the problem to the calculation of the inner-products of two trees.


If we were looking for a fronted subordinate clause, only (c) would be a match.

these differences, and the expected variation from using different transcripts from different sources, account for the difference in our results and Long and Channell's.


For these purposes, and because of its higher content validity, IPSyn scores often tells us more than MLU scores.

scores computed automatically from transcripts parsed as described in section 3 were very close to the scores computed manually.


Our syntactic analysis system takes a sentence and produces a labeled dependency structure representing its grammatical relations.

each one of the sentences has an identical part-speechsequence.


• Nouns in a caption often refer to an entity that is a label on the independent axis.

our corpus study showed that about half of all captions contain some evidence that contributes to understanding the graphic's message, but that fully understanding captions is a difficult problem.


We use this to extract certain benchmarks concerning multilogue interaction.

a special case of this are moderated multilogues, where all dialogues involve a designated individual (who is also responsible for turn assignment.).


When this is not the case because speakers overlap, the tagging scheme encodes synchronous speech by means of an alignment map used to synchronize points within the transcription.

in order to be able to measure the distance between the NSUs encountered and their antecedents, all instances were tagged with the sentence number of their antecedent utterance.


Answer: By definition, certain types of question prompt for certain answers.

clarification requests (cRs) in conversation ensure and maintain mutual understanding and thus play a crucial role in robust dialogue interaction.


The decision concerning what length of phrases to store in the phrase table seems to boil down to a practical consideration: one must weigh the likelihood of retrieval against the memory needed to store longer phrases.

since this linguistic information is not encoded in the model, unseen adjective noun pairs may still be handled incorrectly.


The exceptions to the above are the two glue rules, (13), which has weight one, and (14), which has weight the idea being that λ g controls the model's preference for hierarchical phrases over serial combination of phrases.

each cell contains all the items standing for X spanning f j i .


For all three systems we trained the translation model on the FBIS corpus (7.2M+9.2M words); for the language model, we used the SRI Language Modeling Toolkit to train a trigram model with modified Kneser-Ney smoothing (Chen and Goodman, 1998) on 155M words of English newswire text, mostly from the Xinhua portion of the Gigaword corpus.

we have successfully trained on almost 30M+30M words by tightening the initial phrase length limit for part of the data.


A simpler alternate approach would be to compare bags-ofwords.

we currently use a small set of features reflecting very local information in the dependency tree to model P(pos(m,t) | S, T): • The lexical items of the head and modifier.


Noting that the gap widens at smaller corpus sizes, we suggest that our tree-based approach is more suitable than string-based phrasal SMT when translating from English into languages or domains with limited parallel data.

we make the following simplifying assumption (where c is a function returning the set of nodes modifying t): Furthermore, we assume that the position of each child can be modeled independently in terms of a head-relative position: Figure 3a demonstrates an aligned dependency tree pair annotated with head-relative positions; Figure 3b presents the same information in an alternate tree-like representation.


Of course, the quality ranking provided by QUEEN is meaningless if the similarity metric x does not capture the essential features of the models.

this formula satisfies all formal conditions except 5 (K M,A∪{a} (x) = K M,A∪{a,a} (x)), because it is sensitive to repeated peers.


The source model stays the same, and we still pay a probability cost in the channel model for every subtree deleted.

in these cases sentence pairs, or sections of them, are ignored.


The count of NP → DT JJ NN = 3, and the possible long versions of NP → DT NN are itself (with count of 3) and NP → DT JJ NN (with count of 4), yielding a sum of 7.

their source probabilities are different.


Working together in a virtual community in non-collocated environments, OSS developers communicate and collaborate using a wide range of web-based tools including Internet Relay Chat (IRC), electronic mailing lists, and more (Elliott and Scacchi, 2004).

they utilize an important concept from conversational analysis, adjacent pairs (AP), which consists of initiating and responding utterances from different speakers.


(1999) for parsing the Prague Dependency Treebank, builds on Collins' Model 2 by implementing a 1st order Markov assumption for the generation of sister non-terminals.

a language which has a flexible word order (like German), but a non-flat treebank (like English) should show no effect of lexicalization, i.e., lexicalized models are predicted not to outperform unlexicalized ones.


With the exception of DOP models (Bod, 1995), it is uncommon to smooth unlexicalized grammars.

the Coord GF re-annotation would therefore replace the above rule with NP-OA NP-OA KON NP-OA.


The first 18,602 sentences are used as training data, the following 1,000 form the development set, and the last 1,000 are used as the test set.

the high performance was due to three factors: (i) treebank transformations (ii) an integrated model of morphology in the form of a suffix analyzer and (iii) the use of smoothing in an unlexicalized grammar.


The purpose of this is experiment is not only to improve parsing results, but also to investigate the overall effect of smoothing on parse accuracy.

brants (2000) describes a POS tagger with a highly tuned suffix analyzer which considers both capitalization and suffixes as long as 10 letters long.


our results indicate that the choice of search and smoothing algorithm also add a degree of variance as improvements are added to the parser.


For Hinoki, on average, the number of decisions presented to the annotator was 27.5.

finally Reject Disagreement is those cases were one annotator found no suitable parses, but one selected one or more.


If it is a verb, this gives rise to further structural ambiguity in the relative clause, as discussed in Section 2.

the existing information can be used to blaze 5 trees in the parse forest: that is to select or reject certain discriminants based on existing information.


This ensures the last source symbol is indeed consumed.

additional iterations overtrained our models.


With s i , s i−1 we can further reduce the error rate to 20.26%.

snext i is an RV whose value is set to the symbol at the a i +1 position of the string, i.e., snext i =s a i +1 .


The second direction is to introduce informative priors motivated by linguistic theories.

demoting the constraint that prefers the wrong candidate) that GLA uses to find the target grammar.


Clearly, Max-Ent is a rather different type of model from Stochastic OT, not only in the use of constraint ordering, but also in the objective function (conditional likelihood rather than likelihood/posterior).

by taking advantage of the interdependence of G and Y , the Gibbs sampler provides a Markov chain that converges to p(G, Y |D), which allows us to return to the original subspace and derive p(G|D) -the target distribution.


In our experiment, sampling from truncated normal distributions is realized by using the idea of rejection sampling: to sample from a truncated nor- we first find an envelope density function g(x) that is easy to sample directly, such that π c (x) is uniformly bounded by M • g(x) for some constant M that does not depend on x.

even though the expected model assigns equal values to µ 3 and µ 4 (corresponding to D and B, respectively), µ 3 is always less than µ 4 , simply because there is more chance of penalizing D rather than B.


it is called a Markov Chain Monte Carlo (MCMC) method; see Andrieu et al.


Unsupervised learning experiments also test on the complete test set, but create a training set by first adding documents from the test set (without annotation), then adding documents from the annotated training set (without annotation), and finally adding documents from the unannotated training set.

semi-supervised results for the citations domain are shown in Figure 7.


In these cases, some of the reference translations also use "impact".

the third voting model is a boosting model (Freund and Schapire, 1997), since has consistently turned in very competitive scores on related tasks such as named entity classification (Carreras et al., 2002) .


Word sense disambiguation is often assumed to be an intermediate task, which should then help higher level applications such as machine translation or information retrieval.

the first voting model is a naive Bayes model, since Yarowsky and Florian (2002) found this model to be the most accurate classifier in a comparative study on a subset of Senseval-2 English lexical sample data.


Niu is supported by A*STAR Graduate Scholarship.

the closer the examples, more likely they have similar labels (the global consistency assumption).


The Figures 1, 2, 3 and 4 show the learning curves evaluated on K wsd and K wsd for all the lexical sample tasks.

for the future we plan to integrate such information by adding a tree kernel (i.e.


We found that each enhancement (word clusters, coreference reranking, relation reranking) produced an improvement in F score for each folder, allowing us to reject the hypothesis that these improvements were random at a 95% confidence level.

it leads to a very large feature space and makes it difficult to select linguistically appropriate features for particular analysis tasks.


Finally, Table 6 shows the distributions of errors.

they are also combined with other features: 1) ET12+M1>M2; 2) ET12+M1<M2; 3) HM12+M1>M2; 4) HM12+M1<M2.


A possible difference between text classification and gender classification is that different methods for feature weighting may be appropriate.

the features that when present are most indicative of each gender (positive features) are shown in table 6.


It is unlikely that the speakers knew their conversation partner.

the reverse may be true for gender classification, where the common terms may be the ones that mostly contribute to the gender category.


Proximity information is not taken into account at all, e.g.

aSR lattices do have much better WER -in our case the 1-best WER was 55% whereas the lattice WER was 30% -but the position information is not readily available: it is easy to evaluate whether two words are adjacent but questions about the distance in number of links between the occurrences of two query words in the lattice are very hard to answer.


In practice, having a series of model parameters when the iteration ends, we select the model parameters that yield best alignments on the development corpus.

the search algorithm we proposed is supervised, relying on a hand-aligned bilingual corpus, while the baseline approach of IBM alignments is unsupervised.


In this paper, we first train two models using the out-of-domain training data and the in-domain training data, and then build a new alignment model through linear interpolation of the two trained models.

the data in our corpus are from several manuals describing how to use the diagnostic ultrasound systems.


1 A cept is defined as the set of target words connected to a source word (Brown et al., 1993).

this result is different from that in (Wu and Wang, 2004), where their method achieved an error rate reduction of 21.96% as compared with the method "Gen+Spec".


We think the limitations of the bracketing grammar are another reason for not being able to improve the AER of longer sentence pairs after lexicalization.

for comparison, we also included the results from IBM Model 1 and Model 4.


The independence assumptions typifying S-CFGs are also valid for S-ITGs.

we presented the formal description of a Stochastic Lexicalized Inversion Transduction Grammar with its EM training procedure, and proposed specially designed pruning and smoothing techniques.


First, since the facts were automatically collected, they contain errors and thus the system is tested against wrong answers.

essentially, for any text snippet of the form A 1 pA 2 qA 3 , these systems estimate the probability that a relationship r(p, q) holds between entities p and q, given the interstitial context, as 2 the probability of a relationship r(p, q) is the number of times that pattern xA 2 y predicts any relationship r(x, y) in the training set T .


This sentence contains two people, one job title and two companies.

finally a graph is constructed from the output of this classifier and the complex relations are determined from the cliques of this graph.


This joint classification is shown to improve accuracy of both the entities and relations returned by the system.

all the cliques in the example are shown in Figure 2b.


Next, we tried the composite shallow/POS tag sequence (S2), as in figure 3(c).

these can disrupt what may be termed the intended sequence of syntactic categories in the utterance, so we also tried skipping these constituents when mapping from the parse tree to shallow parse sequences.


German has substantially freer word order than English.

another class of approaches make use of syntactic information in the target language alone, effectively transforming the translation problem into a parsing problem.


All these approaches, though different in formalism, model the two languages using tree-based transduction rules or a synchronous grammar, possibly probabilistic, and using multi-lemma elementary structures as atomic units.

top-NP: the noun phrases that do not have another noun phrase as parent or ancestor.


In most types of statistical machine translation, conditional probability È Ö Á ½ Â ½ µ is used to describe the correspondence between two sentences.

the performance improvement in CBIBM with BiVN was remarkable.


Our training algorithm can easily handle millions of features.

using features such as those described above, we can parameterize the probability of such a sequence as , where ± is a vector of unknown model parameters to be estimated from the training data.


Assume C i is a sentence selected from the corpus to realize P : "This is 3 bedroom 2 bathroom house".

sentences that are too complex are hard to understand, so are sentences lacking semantic cohesion and cross-sentence coherence.


This measure is used to verify our claim that because we use global instead of local optimization, we can avoid generating dangling sentences by making more balanced sentence boundary decisions.

its aggregation and referring expression generation capability).


This measure is used to verify our claim that since we also optimize our solutions based on boundary cost, we can reduce incoherence across multiple sentences.

the baseline approaches did much worse (61% and 21%).


The full list of features is shown in Figure 2, which also identifies possible values and which word classes (POS) can express these features.

we intend to explore to what extent we can transfer models trained on Standard Arabic to dialectal morphological disambiguation.


For each token (base phrase) to be tagged, a set of features is created from a fixed size context that surrounds each token.

we incorporated these features in the systems based on hand-corrected TreeBank parses and Charniak parses respectively.


Training a model which has such huge number of classes is infeasible if the model does not factorize due to strong independence assumptions.

this is similar to adding features from identification and classification models.


We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative loglinear models.

in order to be able to incorporate long-range dependencies in our models, we chose to adopt a re-ranking approach (Collins, 2000), which selects from likely assignments generated by a model which makes stronger independence assumptions.


Figure 2 illustrates how a German phrase can be used as a point of identification for English paraphrases in this way.

barzilay and McKeown (2001) extract both singleand multiple-word paraphrases from a monolingual parallel corpus.


The original formulation of statistical machine translation (Brown et al., 1993) was defined as a word-based operation.

we define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account.


We find these alignments using recent phrase-based approaches to statistical machine translation.

the English phrases that are aligned with the different German phrases (which themselves maybe indicative of different word senses) are mingled.


The purpose of this paper is to explore whether the identification of candidate phrasal terms can be improved by adopting a heuristic which seeks to take certain of these statistical issues into account.

given a phrase like east end, we can rank how often __ end appears with east in comparison to how often other phrases appear with east.That is, if {__ end, __side, the __, toward the __, etc.}


We report the results in Table 3 and Table 4 with beam parameters of (B = 25) and (B = 100) respectively.

experiments show that our method generates cosine similarities between pairs of nouns within a score of 0.03.


If the hamming distance is below a certain predetermined threshold, we output the pair of vectors with their cosine similarity (as calculated by equation 4).

a core component of most clustering algorithms used in the NLP community is the creation of a similarity ma-trix.


Using more sophisticated feature functions.


To perform this evaluation, we randomly choose 100 nouns (vectors) from the web collection.

most language analysis tools are too infeasible to run on the scale of the web.


In the future, we plan to study additional scheme variants that our current results support as potentially helpful.

in principle, we would like to have whatever adjustments now made irrelevant (because of the missing feature) to be removed.


L1 and L2 behaved similar to EN across the different training size.

a pronominal clitic is represented as its feature representation to preserve its uniqueness.


Equations 2, 3, and 5 allow us to evaluate the numerator of the expression in Equation 1.

it also relates to earlier work by (Blei and Moreno, 2001) that uses a topic representation but also does not allow adaptively combining different topics.


The edge weights, w(u, v), define a measure of similarity between pairs of nodes u and v, where higher scores indicate higher similarity.

in our case, the multi-way cut is constrained to preserve the linearity of the segmentation.


In our case, the true positive rate is the fraction of boundaries correctly classified, and the false positive rate is the fraction of non-boundary positions incorrectly classified as boundaries.

we can also represent graph nodes with non-overlapping blocks of words of fixed length.


Traditionally, syntactic information from parse trees is represented as a set of flat features.

the new composite kernel K c , defined as the multiplier of normalized K t and K n , will return a value close to 1 only if both the structured features and the normal features from the two vectors have high similarity under their respective kernels.


For this test, materials from Traxler et al.

it has a predictive power of processing delay not only for lexically ambiguous sentences but also for structurally garden-pathed sentences.


(9) Ambiguous but Non-Garden-Path Control The horse raced past the barn and fell.

the scope of their sentence processing model is limited to the DO/SC ambiguity, and the description of their model is not clear.


This result is also reported in other constraint-based model studies (e.g.

the bias towards past-participle produces a repair in the control sentence, which is unexpected.


Participants were instructed to provide a numeric score that indicates how much better (or worse) the current sentence was compared to a given reference sentence (Example: If the reference sentence was given the reference score of 100, judging a target sentence five times better would result in 500, judging it five times worse in 20).

they can however play a more general role, making it possible to express syntactic information without using other mechanism (such as a generation function).


In this conception, a language (be it natural or not) is produced (or generated) by a grammar by means of a specific mechanism, for example derivation.

one randomization was made of each form.


For example, from the sentence in Figure 1 for the labelling of f 24 = de with a 24 = 16 (for e 16 = of ) we might detect the following feature: Note that it is the target word indexed by a t , rather than the index itself, which determines whether the feature is active, and thus the sparsity of the index label set is not an issue.

we trained Model 4 in both directions on the two language pairs.


These sample documents illustrate an important point: if a document in language L 1 has a set of names, and one finds a document in L 2 containing a set of names that look as if they could be transliterations of the names in the L 1 document, then this should boost one's confidence that the two sets of names are indeed transliterations of each other.

names of the same entity that occur in different languages often have correlated frequency patterns due to common triggers such as a major event.


as we mentioned above, in GIZa-Lex the average number of possible translations for a source word is 12.


However, high LLR scores can indicate either a positive association (i.e. )

for the last stage, however, precision is paramount.


We also adopted their overlap scoring mechanism which treats each gloss as a bag of words and assigns an n word overlap the score of n 2 .

the work described here could be usefully employed in two tasks: (a) to create preliminary annotations, thus supporting the "annotate automatically, correct manually" methodology used to provide high volume annotation in the Penn treebank project; and (b) in combination with supervised WSD methods that take context into account; for instance, such methods could default to an unsupervised system for unseen words or words with uninformative contexts.


As the semantic method outperformed the lexical overlap in the evaluations of previous Section, we decided to acquire a clustering on the entire WordNet sense inventory using this approach.

• RACING A series of such competitions for horses or dogs • A situation in which individuals or groups compete (→ contest) • AS-TRONOMY The course of the sun or moon through the heavens (→ trajectory).


The figures are shown in Table 3 (good entropy and purity values should be close to 0 and 1 respectively).

the random setting obtained 64.56% average accuracy, while the performance when adopting our automatic clustering was 70.84% (1,025/1,447 items).


To overcome the data sparseness problem, generative models typically applied some smoothing techniques to integrate different scales of contexts in parameter estimation, e.g.

zelenko et al (2003) proposed extracting relations by computing kernel functions between parse trees.


To overcome the data sparseness problem, various scales of sub-trees are applied in the tree kernel computation.

the final hierarchical learning strategy using the class hierarchy built with the all-level hybrid approach achieves F-measure of 58.2 in F-measure, which outperforms the final flat strategy by 2.2 in Fmeasure.


Then the label propagation algorithm consists the following main steps: Step1 : Initialization • Set the iteration index t = 0; • Let Y 0 be the initial soft labels attached to each vertex, where L be the top l rows of Y 0 and Y 0 U be the remaining u rows.

this label propagation process will make the labeling function smooth on the graph.


The testing set was used for final evaluation.

the LP algorithm based on JS divergence consistently outperforms the LP algorithm based on Cosine similarity.


C-structures define the syntactic representation and f-structures define more semantic representation.

the object of the causative (causee -that who is caused by the causer -the subject of the causative verb) is unified with the subject the inner f-structure.


The filler category is abbreviated to "WH" if the rest is identical to the trace category.

the empty elements are here generated by the grammar.


To test the impact of the emotion annotation level, we define the Emotional/Non-Emotional annotation based on our two emotional dimensions: neutral turns on both the FAH and the CERT dimension are labeled as neutral 2 ; all other turns were labeled as emotional.

four labels were used for this dimension: certain, uncertain (e.g.


Not only is there no interaction between REJ (-1) and CERT, but the inclusion of the CERT dimension in the EnE annotation decreases the strength of the interaction between REJ and FAH (the χ 2 value decreases from 409.31 for FAH to a mere 6.19 for EnE).

we define the EnE variable with two values: Emotional and Neutral.


In Table 2, we give examples of our tags.

we learn models for predicting dialog acts of utterances, and models for predicting subtask structures of dialogs.


In the same spirit as the above studies, we first propose a generalization framework for CRF training that allows us to optimize directly not only the error rate, but also any evaluation measure.

the framework of MCE criterion training supports the theoretical background of our method.


For MAP, MCE and MCE-F, we used the L 2norm regularization.

it has been shown (Le Roux and McDer-mott, 2005) that even simple gradient-based (firstorder) optimization methods such as GPD and (approximated) second-order methods such as Quick-Prop (Fahlman, 1988) and BFGS-based methods have yielded good experimental optimization results.


In contract, in BLasso, as shown in Equation 12, the optimization over δ is removed, and for each feature, its loss is calculated with an update of either +ε or -ε, i.e., the grid search is used for feature selection.

it scales the incremental step δ by a small constant ν, ν ∈ (0, 1).


To our knowledge, this paper presents the first empirical study of lasso for a realistic, large scale task: LM for Asian language text input.

similar to speech recognition, IME is viewed as a Bayes decision problem.


The LUT is designed to measure the ability to grasp the correct meaning of words that are the elements of a sentence, and to understand the composition and the summary of a text.

we therefore assumed for the time being that nouns having readings of 6 or more characters were big words, and with this as a guideline, we again measured the percentage of nouns in a document that were big words.


These metrics are broken down further into various statistical quantities in the following sections.

we found that Jess could return a low evaluation of "content" even for an essay that responded well to the essay prompt.


These unclassified instances are tagged with the symbol "?".

the default rules were used about 60% and 30% of the time in "DL (EDR)" and "DL (BNC)", respectively; when only the default rules were used, "DL (EDR)" ("DL (BNC)") achieved 0.66 (0.56) in recall and 0.58 (0.53) in precision.


Note that the rules are divided into two columns for the purpose of illustration in Table 2; in practice, they are merged into one.

this suggests that the mass count distinction is often determined by words surrounding the target noun.


The following is one of the homework for last week.

we expect to be looking into alternative word alignment models and possibly enhancing our system's decoder using some of the richer, more structured language models that are beginning to emerge.


To avoid this, all dependents to the left of the rightmost verb will depend on the leftmost verb, whereas the others will depend on the rightmost verb.

nivre and nilsson (2005) improve parsing accuracy for MaltParser by projectivizing training data and applying an inverse transformation to the output of the parser, while Hall and novák (2005) apply post-processing to the output of Charniak's parser (Charniak, 2000).


Table 2 shows the most low-and highinformation rows from English and Swedish (Dyen et al's (1992) data).

the probability of confusion of two words depends solely on their similarity distance.


In order to obtain curves as the IR engine improves, we have linearly interpolated the Inquery rankings with the true relevance judgments.

of all the systems compared, only BAYESUM and the KL+REL model use the relevance judgments; however, they both have access to exactly the same information.


The pertinence of pattern i P to a word pair j j Y X : is the expected relational similarity between a word pair k k Y X : , randomly selected from W according to the probability distribution ) : , and the word pair can be interpreted as the degree to which the pair k k Y X : is representative (i.e., typical) of pairs that fit the pattern i P .

another challenge is to find a way to empirically evaluate the performance of any such pattern ranking algorithm.


These three algorithms can answer the SAT questions, but they do not provide any kind of explanation for their answers.

with this work, our algorithm is not restricted to nominalizations.


Integrating a number of them into a single systems poses the problem of error propagation.

we are able to show that the potential of wCDG for information fusion is strong enough to accomodate even rather unreliable information from a wide range of predictor components.


As we have shown, parsing large corpora allows to set up error mining techniques, so as to identify missing and erroneous information in the different resources that are used by full-featured parsing systems.

to begin the whole process, we just say, for an occurrence o i,j of sen- this means that for a non-parsable sentence, we start from a baseline where all of its occurrences have an equal probability of being the cause of the failure.


The whole system has been tested on parsing results produced for several multi-million-word corpora and with two different parsers for French, namely SXLFG and FRMG.

these annotations, aimed to ease the analysis of the error mining results by linguists and by the developers of parsers and resources (lexica, grammars), are saved in a database (SQLItE).


The second technique is self-training -parsing unlabeled data and adding it to the training corpus.

furthermore, the treebanked Brown data is mostly general non-fiction and much closer to WSJ than, e.g., medical corpora would be.


transitivity, passivisability) which have been extracted from corpora using e.g.

also a few semantically different verbs clustered wrongly because of this reason, such as the ones exemplified in C. In C, immunoblot (from the LABEL class) is still somewhat related to wash and rinse (the WASH class) because they all belong to the larger EXPERIMENTAL PRO-CEDURES class, but overlap (from the PROCESS class) shows up in the cluster merely because of syntactic idiosyncracy.


The values d = 1000 and = 5 were found to produce the best results.

this is equivalent to a column reordering in a matrix where the rows are the terms and the columns the bits.


Table 2 shows the INVR scores for our various techniques using the BNC with cut-offs of 5 and 100.

the angular similarity gives the cosine by the probability can be derived from the Hamming distance: By combining equations 5 and 6 we get the following approximation of the cosine distance: the cosine of two context vectors is approximated by the cosine of the Hamming distance between their two signatures normalised by the size of the signatures.


Unfortunately, identifying the rhetorical relation between two sentences has been a difficult task for computers.

we define the notation A B to show that segment A precedes segment B.


Events with the same hypernyms may have similar durations.

for a given event, both the head of its subject and the head of its object are extracted from the parse trees generated by the CONTEX parser.


As in (Krippendorff, 1980), we assume there exists one global distribution for our task (i.e., the duration ranges for all the events), and "chance" annotations would be consistent with this distribution.

since it's natural to assume the most likely duration for such distribution is its mean (average) duration, and the distribution flattens out toward the upper and lower bounds, we use the normal or Gaussian distribution to model our duration distributions.


The kappa statistic (Krippendorff, 1980;Carletta, 1996) has become the de facto standard to assess inter-annotator agreement.

human agreement on this more finegrained task is low (44.4%).


For SVM and C4.5, all features are converted into binary features (6665 and 12502 features).

none of the techniques directly apply to our data, which are ranges of durations from a lower bound to an upper bound.


For the experiments, we used the Recognizing Textual Entailment Challenge data sets, which we name as follows: -D1, T 1 and D2, T 2, are the development and the test sets of the first  and second (Bar Haim et al., 2006) challenges, respectively.

anchors are mainly used to extract relevant syntactic subtrees between pairs of text and hypothesis.


Although the textual entailment recognition problem is not new, most of the automatic approaches have been proposed only recently.

a treekernel function over t 1 and t 2 is K T (t 1 , t 2 ) = , where N t 1 and N t 2 are the sets of the t 1 's and t 2 's nodes, respectively.


A treekernel function over t 1 and t 2 is K T (t 1 , t 2 ) = , where N t 1 and N t 2 are the sets of the t 1 's and t 2 's nodes, respectively.

to keep track of the argument movements, we augment the node labels with placeholders.


For our first model, this context is the previous sentence.

overall, we find that the modifications we introduced to model the parallelism effect in humans have a positive, but small, effect on parsing accuracy.


This resulted in the following four conditions: (2) a. DT JJ NN and DT JJ NN (parallel) Hilda noticed a tall man and a strange woman when she entered the house.

because of the second problem noted above, in Section 4, we simulated the context by filling the cache with rules from the correct tree.


The increase in recall is statistically significant, and it shows classifier stacking can improve performance.

they reported up-to-date the best parsing performance on Chinese treebank.


A simple transformation process as described in (Sagae and Lavie, 2005) is employed to convert between arbitrary branching trees and binary trees.

the classifier ensemble model and SVM model of our parser achieved second and third highest LP, LR and F1 for sentences ≤ 100 words as shown in Table 3.


This is a 27% reduction in error and a significant reduction in size 1 over the most accurate gram- mar in Matsuzaki et al.

we take a different, fully automated approach.


In the limit, each word may well have its own unique syntactic behavior, especially when, as in modern parsers, semantic selectional preferences are lumped in with traditional syntactic trends.

the relevant rule is the very probable VP-2 → VP-2 ADVP-6; adding this ADVP to a growing VP does not change the VP subsymbol.


For example, a noun phrase in subject position certainly has a distinct distribution, but it may benefit from being smoothed with counts from all other noun phrases.

this initialization is the absolute minimum starting grammar that includes the evaluation nonterminals (and maintains separate grammar symbols for each of them).


For a grammar with 479 subcategories (4 SM cycles), lowering the threshold to e −15 led to an F 1 improvement of 0.13% (89.03 vs. 89.16) on the development set but increased the parsing time by a factor of 16.

it also has a very low parsing performance: 65.8/59.8 LP/LR on the development set.


when the two words cannot be substituted) is less interesting.

the obtained figures, reported in table 3, are clearly below the baseline, suggesting that simple unsupervised indirect strategies cannot be used for this task.


The remainder of the paper is organized as follows.

it can use a largescale monolingual corpus for training to solve the data-sparseness problem.


It would be useful if the model could utilize the (non-adjacent) information about "Sp1" being "protein" to classify "CP1" as "protein".

for example, the best f-score in the shared task of Bio-NER in COLING 2004 JNLPBA (Kim et al., 2004) was 72.55% (Zhou and Su, 2004) 1 , whereas the best performance at MUC-6, in which systems tried to identify general named entities such as person or organization names, was an accuracy of 95% (Sundheim, 1995).


P (s|λ) can be calculated efficiently using the forward-backward procedure which makes the estimate for the total probability of all state paths that go through filler states straightforward to be: Now it is clear to see that, if the calculated P (s, Q f iller |λ) > P (s, Q bg |λ), then segment s is considered more likely to have filler occurrence(s).

the effect of the segmentbased HMM extraction goes beyond applying a post-processing step to the document-based HMM extraction, since the latter can only reduce the redundancy but not improve the F1 scores.


Compared to the document-based HMM IE modelling, the extraction performance on location is significantly improved by our segment HMM IE system.

it trains an HMM from labelled segments in texts, and then use the learned HMM to determine whether a segment is relevant or not with regard to a specific extraction task.


Another interesting topic is the complementarities between both mining systems.

even where resources are available, such as for english-French, the data are usually restricted to government documents (e.g., the Hansard corpus, which consists of French-english translations of debates in the Canadian parliament) or newswire texts.


Similarly, (Koehn et al., 2003) propose a relative distortion model to be used with a phrase decoder.

we also propose a novel metric to measure word order similarity (or difference) between any pair of languages based on word alignments.


The number of references used is also reported (e.g., BLEUr1n4c: r1 means 1 reference, n4 means upto 4-gram are considred, c means case sensitive).

they reported results that are better than word-based IBM4-like decoder.


First, at the word level, we can do stemming, do stop-word pruning, include POS (Part of Speech) information, etc.

we choose it here as one of the representative classifiers to be examined.


This implies that the synset index structure is the same for the two languages.

in this paper we have shown that the problem of cross-language text categorization on comparable corpora is a feasible task.


In our experiments we exploit two alternative multilingual resources: Mul-tiWordNet and the Collins English-Italian bilingual dictionary.

when bilingual dictionaries are available the performance of the categorization gets close to that of monolingual text categorization.


In section 2, we briefly review the CME modeling and SGC algorithm.

furthermore, the experiments in Zhou et al.


The gain for a feature needs to be re-computed only when the feature reaches the top of a priority queue ordered by gain.

we also show that symbolic prosody labels together with confidence scores are useful in edit region identification task.


The functions split() and merge() are used to split and merge the feature space respectively.

the possible gain each feature may add to the model gets smaller.


(2005) do to evaluate our system PAIR and recruit 9 students who have different backgrounds to participate in our experiment.

the participants would judge which of these results are relevant.


For each of these actions,  The Iterative Algorithm module implements the HITS-like algorithm described in section 2.

their work differs from ours in three ways: First, they use the cosine similarity to implement query expansion, and use Rocchio formulation (J. J. Rocchio, 1971) to re-rank the search results.


In section 4, we describe our experimental method.

to focus our study on the relationship between Mt performance and retrieval effectiveness, we do not use techniques such as pseudo-relevance feedback although they are available and are known to improve IR performance.


Compared with dictionary or corpus based methods, the advantage of MT-based query translation lies in that technologies integrated in MT systems, such as syntactic and semantic analysis, could help to improve the translation accuracy (Jones et al., 1999).

the correlations are above 95% for all of the four runs, which means in general, a better performance on Mt will lead to a better performance on retrieval.


Their English-Chinese CLIR experiments on TREC 5&6 Chinese collections showed that the initial retrieval performance increased sharply with lexicon size but the performance was not improved after the lexicon exceeded 20,000 terms.

recent research work on CLIR shows a trend to adopt MT-based query translation.


Thus, in practice the rate of growth on the x-axis of Figure 1 will slow as the corpus size increases.

in addition to exploring sensitivity with respect to a random subset, we were interested in learning more about the relationship between the global iDF and the iDF calculated on a journal sub-set.


The chunking states included in the NE classes were: BEGIN (beginning of a NE), MIDDLE (middle of a NE), END (ending of a NE), and SINGLE (a single-word NE).

in text-based NER, better results are obtained using discriminative schemes such as maximum entropy (ME) models (Borthwick, 1999;Chieu and Ng, 2003), support vector machines (SVMs) (Isozaki and Kazawa, 2002), and conditional random fields (CRFs) (McCallum and Li, 2003).


A typical syntactic feature is the path on a parse tree from a target predicate to a noun phrase in question (Gildea and Jurafsky, 2002;Carreras and Marquez, 2005).

the comparison between BM (SCM) with BM StR (SCM StR) indicates that introducing the structural information effectively contributes to this task.


If it is less than EXP , then this function returns 1 and trigrams are used instead of bigrams.

adapting features or contexts has been an important issue in language modeling (Siu and Ostendorf, 2000).


The basic rule asserts that all content words should be spaced.

in addition, the classconditional distribution after expanding could be very different from that before expanding due to data sparseness.


When only the right context is used, the accuracy gets 88.26% which is worse than the left context only.

if higher order Ò-gram is adopted for better accuracy, the volume of corpora required will be increased exponentially.


The number of errors made by expanding first is 108,831 while that by shrinking first is 114,343.

even though word spacing is one of the important tasks in Korean information processing, it is just a simple task in many other languages such as english, German, and French.


The non-zero return value of HowLargeExpand implies that the window size of the current Ò-grams should be enlarged.

there are many Ò-grams of which frequency is zero.


In this paper, an expanding is checked prior to a shrinking as shown in Figure  4.

the likelihood of È´Û ½ Û Ý µ is expanded to Since the coefficients of Equation (4) were determined arbitrarily (Kang, 2004), they are replaced with parameters « of which values are determined using a held-out data.


As it can be observed, 77% of English terms from Korean web pages and 83% of English terms from Chinese Web pages can be strictly translated into accurate Korean and Chinese, respectively.

if n m ≤ , all adjacent sequential combinations of words within the window are treated as candidates 2.


For instance, Korean words "디지탈", "디지틀", and "디지털" are found in local Web pages, which all correspond to the English word "digital" but are in different forms because of different phonetic interpretations.

we do it the other way around by choosing the English phrases from the local Web pages based on a certain selection criteria.


Phonetic weight is the transliteration probability between English and candidates in local language.

another acceptable translation "绩优股 (a kind of stock)" is ignored.


Since we observed (see Section 2.2) that Euclidean distance constraint does not add significantly to the performance, we propose not to use this constraint for English-Hindi word alignment.

for the present work this has been done manually for Hindi.


But the Hindi translation of "black horses" is kaale ghode; whereas "black mare" is translated as kaalii ghodii.

the algorithm provided by Somers works by first finding all the minimum score word pairs using dynamic programming, and then applying three filters Multiple Alignment Selection filter, Best Alignment Score Selection filter and Frequency Range constraint to the raw results to increase the accuracy of alignment.


Loanwords are also inflected in all of these types, except for phrase (b).

in phrases (b)-(e) in Figure 2, the content words are inflected.


The n r term is derived by integrating over all possible values of p $ , and noting that the total number of U productions in h − is n − + 1.


Since h 1 and h 2 are the same except for a few rules, this is straightforward.

this paper demonstrates the importance of contextual dependencies for word segmentation by comparing two probabilistic models that differ only in that the first assumes that the probability of a word is independent of its local context, while the second incorporates bigram dependencies between adjacent words.


For Levantine, we use a similarly annotated corpus, the Levantine Arabic Treebank (LATB) from the Linguistic Data Consortium.

morphological Behavior Classes: The mBCs are variant-independent, so in theory no changes needed to be implemented.


We want to tag phrases that are complete in their syntactic structure, avoid the requirement of tagging recursive structures that include full clauses (relative clauses for example) and in general, tag phrases that have a simple denotation.

we found the combination of the Number and the Construct features to be most effective in improving chunking results.


POS tags are very informative features for the supertagger, which suggests that using a multi-POS tagger may benefit the supertagger (and ultimately the parser).

what we would like to do is maintain some degree of POS tag ambiguity and pass multiple POS tags through to the supertagging stage (and eventually the parser).


The mean field approximation was used for inference in their method.

local 3 A major method for generating such pseudo unknown words is to collect the words that appear only once in a corpus (Nagata, 1999).


suffixes and character types) of the unknown words.

the increases of the accuracies on the English corpora (the GEN and SUS corpora) were small.


The method models a whole document by considering interactions between POS tags of unknown words with the same lexical form.

the method models the whole document and finds a set of parts-of-speech by maximizing its conditional joint probability given the document, rather than independently maximizing the probability of each part-of-speech given each sentence.


Using these probabilities we regard the corpus as a stochastically segmented corpus (SSC).

when Û is outside of the vocabulary Ï, Nagata (1994) proposed a stochastic word segmenter based on a word Ò-gram model to solve the word segmentation problem.


In (Wright et al., 1997) phrase level significance was obtained for noisy transcribed data where the phrases are clustered and combined into finite state machines.

based on the hierarchical model described in Section 4 and topic identification mentioned in the last sub-section we describe  (1) a tool capable of aiding agents for efficient handling of calls to improve customer satisfaction as well as to reduce call handling time, (2) an administrative tool for agent appraisal and training.


It can be seen that in topic cluster-1, which is about expense reimbursement and related stuff, most of the queries can be answered quickly in standard ways.

we extract all n-grams which occur more frequently than a threshold and do not contain any stopword.


Based on the topic level information we can check whether the agent identified the issues and offered the known solutions on a given topic.

the documents contain well formed sentences which allow for parsers to be used.


In this model the correspondence h is a synchronous parse tree over input and output sentences, and features include the scores of various productions used in the tree.

another difference between bold updating and local updating is that the BLEU score on the training data is dramatically higher for bold updating than for local (or hybrid) updating: 80 for the former versus 40 for the latter.


We present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited.

the last column of table 3 shows the performance of our methods on the final tESt set.


We first discuss several challenges to error-driven discriminative approaches.

we then present a novel way to parameterize and introduce learning into the initial phrase extraction process.


(2005) compared their results with Model 4 using "intersection" by looking at AER (with the "Sure" versus "Possible" link distinction), and restricted themselves to considering 1-to-1 alignments.

(Fraser and Marcu, 2006) established that tuning the trade-off between Precision and Recall in the F-Measure formula will lead to the best BLEU results.


Our method directly learns such serialization rules from a bilingual corpus without linguistic clues.

a phrase-based translation model is one of the modern approaches which exploits a phrase, a contiguous sequence of words, as a unit of translation (Koehn et al., 2003;Zens and Ney, 2003;Tillman, 2004).


(2000), Wermter and Hahn (2004)), however, have already observed that ranking the candidates merely by their frequency of occurrence fares quite well compared with various more sophisticated association measures (AMs such as t-test, loglikelihood, etc.).

this local "spilling over" to the upper portion is comparatively small considering the change that occurs with respect to LSM.


In this paper, we propose a measure of similarity to capture this intuition.

for each relation instance (x, r, y), two human annotators selected from all sense permutations of x and y the correct attachment points in WordNet.


Time sequence scoring is then used to rerank the list and choose the candidate best temporally aligned with the NE.

nEs have similar time distributions across such corpora, and often some of the tokens in a multi-word nE are transliterated.


While generative models are often robust, they tend to make independence assumptions that do not hold in data.

time sequence similarity based approaches would incorrectly match words which happen to have similar time signatures (e.g., Taliban and Afghanistan in recent news).


This may be due to two reasons: 1) the dependency tree (Culotta and Sorensen, 2004) and the shortest path (Bunescu and Mooney, 2005) lack the internal hierarchical phrase structure information, so their corresponding kernels can only carry out node-matching directly over the nodes with word tokens; 2) the parse tree kernel has less constraints.

in this section, we define the composite kernel and study the effective representation of a relation instance.


We also added our original features and their combinations, with reference to (Sassano, 2004;Kudo and Matsumoto, 2002), but we removed the features that had a frequency of less than 30 in our training data.

we think correct probability estimation using an occurrence ordered set is difficult, because it gives rise to an explosion in the number of combination, The evaluation data we used was Kyodai Corpus 3.0, a corpus manually annotated with dependency relations (Kurohashi and Nagao, 1998a).


The third, which we term oracle round-robin, selects the first abstract from each of the three best clusters (also determined by an oracle).

of these findings, our work focuses on MEDLINE abstracts as the source for answers.


The overall procedure to verify if an entailment between two verbs (v t , v h ) holds in a pointwise assertion is: whenever it is possible to apply the agentive nominalization to the hypothesis v h , scan the corpus to detect those expressions in which the agentified hypothesis verb is the subject of a clause governed by the text verb v t .

in this case, the temporal relation is thus inverted with respect to backward-presupposition, since v t precedes v h .


3, the entailment relation is a semantic relation defined at the synset level, standing in the verb sub-hierarchy.

it is possible to derive from the 409 entailment synset a test set of 2,233 verb pairs.


1 as P pe , where verb form variability is taken into consideration.

this rule applies to verb logical subjects: if a verb v has a selectional restriction requiring its logical subjects to satisfy a property c, it follows that the implication: should be verified for each logical subject x of the verb v. The implication can also be read as: if x has the property of doing the action v this implies that x has the property c. For example, if the verb is to eat, the selectional restrictions of to eat would imply that its subjects have the property of being animate.


For each event in the event structure of a rewritten story, a search for a match in the important event structure is taken.

most advice rated as "good" was the advice generated by "excellent" stories, which generate less advice than other types of stories.


This choice of objective R Θ is motivated by Ng (2004), who suggests that, given a learning setting where the number of irrelevant features is exponential in the number of training examples, we can nonetheless learn effectively by building decision trees to minimize the 1regularized log-loss.

(This convention was established by Magerman (1995).)


This experiment, labeled PCFG × NONE in figure 4, resulted in an average labeled F 1 of 26.3 and an unlabeled F 1 of 45.7.

to evaluate these learned grammars, we must map the models' phrase types to target phrase types.


For example, DT NN is a perfectly reasonable example of a noun phrase, but is not a constituent when it is part of a longer DT NN NN constituent.

we therefore combine the CCM with our featureaugmented PCFG, denoted by PROTO in experiment names.


Then, candidate answers are ranked by partial matching to the syntactic relation patterns using tree kernel.

modifier similarity Sim(m i , m j ) only consider the morphological and format variations.


Furthermore, since shorter path indicates closer relation between two phrases, we discount path correlation in long question path by dividing the correlation by the length of the question path.

synPattern(0.36MRR), strict-Pattern(0.36MRR), ApprMatch(0.42MRR) and CorME (0.47MRR) which capture syntactic information, perform much better than Density.


VerbNet syntactic patterns allow predicate arguments to be propagated along lexical chains.

the weight of a lexical chain is computed using the following formula inspired by (Moldovan and Novischi, 2002): where n represents the number of relations in the lexical chain.


The weight of a lexical chain is computed using the following formula inspired by (Moldovan and Novischi, 2002): where n represents the number of relations in the lexical chain.

the type of the start structure can be different than the type of the target structure.


The most significant causes for this performance decay were highly ambiguous and unseen predicates (i.e.

for example, two different types of diathesis alternations are the following: [ Subject , P atient The cup] broke.


Also, in the next section we will show that our current mapping achieves very good results, giving evidence for the effectiveness of the Levin class feature.

we used sections from 02 to 22 (52,172 predicates) to train the ILC classifiers and Section 23 (2,742 predicates) for testing purposes.


This is quite intuitive as the semantics of many roles strongly depends on the focused frame.

this validation process has several sub-steps: First, we make use of the property (2) of the Levin classes and FN frames presented in the previous section.